Journal
JOURNAL OF TIME SERIES ANALYSIS
Volume 35, Issue 3, Pages 282-297Publisher
WILEY-BLACKWELL
DOI: 10.1111/jtsa.12064
Keywords
random coefficient autoregression; weighted estimation; Empirical likelihood method
Funding
- NSF [NSF DMS-1005336]
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The consistency of the quasi-maximum likelihood estimator for random coefficient autoregressive models requires that the coefficient be a non-degenerate random variable. In this article, we propose empirical likelihood methods based on weighted-score equations to construct a confidence interval for the coefficient. We do not need to distinguish whether the coefficient is random or deterministic and whether the process is stationary or non-stationary, and we present two classes of equations depending on whether a constant trend is included in the model. A simulation study confirms the good finite-sample behaviour of our resulting empirical likelihood-based confidence intervals. We also apply our methods to study US macroeconomic data.
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